9,635 research outputs found
Recent Advances in Novel Materials and Techniques for Developing Transparent Wound Dressings
Optically transparent wound dressings offer a range of potential applications
in the biomedical field, as they allow for the monitoring of wound healing
progress without having to replace the dressing. These dressings must be
impermeable to water and bacteria, yet permeable to moisture vapor and
atmospheric gases in order to maintain a moist environment at the wound site.
This review article provides a comprehensive overview of the types of wound
dressings, novel wound dressing materials, advanced fabrication techniques for
transparent wound dressing materials, and the key features and applications of
transparent dressings for the healing process, as well as how it can improve
healing outcomes. This review mainly focuses on representing specifications of
transparent polymeric wound dressing materials, such as transparent electrospun
nanofibers, transparent crosslinked hydrogels, and transparent composite
films/membranes. Due to the advance properties of electrospun nanofiber such as
large surface area, enable efficient incorporation of antibacterial molecules,
a structure similar to the extracellular matrix, and high mechanical stability,
is often used in wound dressing applications. We also highlight the hydrogels
or films for wound healing applications, it's promote the healing process,
provide a moisture environment, and offer pain relief with their cool,
high-water content, excellent biocompatibility, and bio-biodegradability.Comment: N
Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression.
For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired.
In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Terminology and ontology development for semantic annotation : A use case on sepsis and adverse events
publishedVersio
Analysis and Design of Detection for Liver Cancer using Particle Swarm Optimization and Decision Tree
Liver cancer is taken as a major cause of death all over the world. According to WHO (World Health Organization) every year 9.6 million peoples are died due to cancer worldwide. It is one of the eighth most leading causes of death in women and fifth in men as reported by the American Cancer Society. The number of death rate due to cancer is projected to increase by45 percent in between 2008 to 2030. The most common cancers are lung, breast, and liver, colorectal. Approximately 7, 82,000 peoples are died due to liver cancer each year. The most efficient way to decrease the death rate cause of liver cancer is to treat the diseases in the initial stage. Early treatment depends upon the early diagnosis, which depends on reliable diagnosis methods. CT imaging is one of the most common and important technique and it acts as an imaging tool for evaluating the patients with intuition of liver cancer. The diagnosis of liver cancer has historically been made manually by a skilled radiologist, who relied on their expertise and personal judgement to reach a conclusion. The main objective of this paper is to develop the automatic methods based on machine learning approach for accurate detection of liver cancer in order to help radiologists in the clinical practice. The paper primary contribution to the process of liver cancer lesion classification and automatic detection for clinical diagnosis. For the purpose of detecting liver cancer lesions, the best approaches based on PSO and DPSO have been given. With the help of the C4.5 decision tree classifier, wavelet-based statistical and morphological features were retrieved and categorised
Looking before we leap: Expanding ethical review processes for AI and data science research
This is the final version. Available from The Ada Lovelace Institute via the DOI in this record. As part of this work, the Ada Lovelace Institute, the University of Exeter’s Institute for Data Science and Artificial Intelligence, and the Alan Turing Institute developed six mock AI and data science research proposals that represent hypothetical submissions to a Research Ethics Committee. An expert workshop found that case studies are useful training resources for understanding common AI and data science ethical challenges. Their purpose is to prompt reflection on common research ethics issues and the societal implications of different AI and data science research projects. These case studies are for use by students, researchers, members of research ethics committees, funders and other actors in the research ecosystem to further develop their ability to spot and evaluate common ethical issues in AI and data science research.Alan Turing InstituteArts and Humanities Research Counci
A Framework for Size-dependent Structural Analysis of Smart Micro/nanoplates
This age has witnessed a proliferation of technological advancements that affected all facets of civilisation. Driven by the joint force of the evolution of sophisticated design tools, tailored material characteristics, and robust mechanics-based analyses, smart composite materials are widely used in high-performance engineering applications. Meanwhile, there is a growing interest in micro/nanoscopic structures in academia and industry due to the overwhelming trend toward portability, miniaturisation and integration in engineering. Therefore, the theoretical, computational, and experimental research communities have developed various effective methodologies to understand the structural behaviour of smart small-scale structures comprehensively.
This dissertation introduces two size-dependent continuum theories, modified strain gradient and nonlocal strain gradient theories, to build the analytical framework for exploring application-driven micro/nanoplates made of smart composite materials. As examples of promising candidates for power supply and nano/microelectromechanical systems, organic solar cells and thermo-magneto-elastic sandwich nanoplates are studied. Size-dependent continuum models combined with various shear deformation plate theories are adopted to derive the governing equations. The size-sensitive static and dynamic mechanical responses, including bending, buckling, and free vibration behaviours of these ultra-fine-size structures, are predicted by capturing the size effect with material length scale or nonlocal parameters. The numerical results underlying size-dependent theories pose a new insight into the structural analysis of functional micro/nanoscopic plate-like structures. Some typical size-involving mechanical characteristics are revealed by comparing the present estimation with those from size-independent models. Moreover, the simulation outcomes thoroughly investigate several practical factors, such as boundary conditions, geometric configuration, and elastic foundation modelling parameters.
In this endeavour, taking advantage of the computational efficiency and accessible operation of nonclassical continuum-based theories, the current analytical framework is suitable for exploring the size-tendency of the smart micro-/nanosized structures. The present work may serve as a benchmark for following numerical simulations and as a guide for evolving new engineering tools for modelling relevant responses by designers and manufacturers
Computational Geometry Contributions Applied to Additive Manufacturing
This Doctoral Thesis develops novel articulations of Computation Geometry for applications on Additive Manufacturing, as follows:
(1) Shape Optimization in Lattice Structures. Implementation and sensitivity analysis of the SIMP (Solid Isotropic Material with Penalization) topology optimization strategy. Implementation of a method to transform density maps, resulting from topology optimization, into surface lattice structures. Procedure to integrate material homogenization and Design of Experiments (DOE) to estimate the stress/strain response of large surface lattice domains.
(2) Simulation of Laser Metal Deposition. Finite Element Method implementation of a 2D nonlinear thermal model of the Laser Metal Deposition (LMD) process considering temperaturedependent material properties, phase change and radiation. Finite Element Method implementation of a 2D linear transient thermal model for a metal substrate that is heated by the action of a laser.
(3) Process Planning for Laser Metal Deposition. Implementation of a 2.5D path planning method for Laser Metal Deposition. Conceptualization of a workflow for the synthesis of the Reeb Graph for a solid region in ℝ" denoted by its Boundary Representation (B-Rep). Implementation of a voxel-based geometric simulator for LMD process. Conceptualization, implementation, and validation of a tool for the minimization of the material over-deposition at corners in LMD. Implementation of a 3D (non-planar) slicing and path planning method for the LMD-manufacturing of overhanging features in revolute workpieces.
The aforementioned contributions have been screened by the international scientific community via Journal and Conference submissions and publications
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